{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# imports\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy\n",
    "from keras.optimizers import Adam\n",
    "import keras\n",
    "from matplotlib import pyplot\n",
    "from keras.callbacks import EarlyStopping\n",
    "import pandas as pd \n",
    "from sklearn.preprocessing import LabelEncoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data from csv file for training and validation data\n",
    "TrainingSet = numpy.genfromtxt(\"./training.csv\", delimiter=\",\", skip_header=True)\n",
    "ValidationSet = numpy.genfromtxt(\"./validation.csv\", delimiter=\",\", skip_header=True)\n",
    "\n",
    "# Split into input (X) and output (Y) variables\n",
    "X1 = TrainingSet[:,0:6]\n",
    "Y1 = TrainingSet[:,6]\n",
    "\n",
    "X2 = ValidationSet[:,0:6]\n",
    "Y2 = ValidationSet[:,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 232 samples, validate on 76 samples\n",
      "Epoch 1/10000000\n",
      " - 1s - loss: 404.3374 - val_loss: 309.6100\n",
      "Epoch 2/10000000\n",
      " - 0s - loss: 375.2555 - val_loss: 294.8144\n",
      "Epoch 3/10000000\n",
      " - 0s - loss: 359.7111 - val_loss: 285.7164\n",
      "Epoch 4/10000000\n",
      " - 0s - loss: 351.3803 - val_loss: 280.9324\n",
      "Epoch 5/10000000\n",
      " - 0s - loss: 347.6015 - val_loss: 278.0652\n",
      "Epoch 6/10000000\n",
      " - 0s - loss: 344.8125 - val_loss: 275.5017\n",
      "Epoch 7/10000000\n",
      " - 0s - loss: 341.8539 - val_loss: 272.8559\n",
      "Epoch 8/10000000\n",
      " - 0s - loss: 338.6591 - val_loss: 270.0061\n",
      "Epoch 9/10000000\n",
      " - 0s - loss: 335.5329 - val_loss: 266.7927\n",
      "Epoch 10/10000000\n",
      " - 0s - loss: 331.8977 - val_loss: 263.3298\n",
      "Epoch 11/10000000\n",
      " - 0s - loss: 327.9559 - val_loss: 259.5031\n",
      "Epoch 12/10000000\n",
      " - 0s - loss: 323.5365 - val_loss: 255.3663\n",
      "Epoch 13/10000000\n",
      " - 0s - loss: 318.8910 - val_loss: 250.9605\n",
      "Epoch 14/10000000\n",
      " - 0s - loss: 313.6974 - val_loss: 246.3042\n",
      "Epoch 15/10000000\n",
      " - 0s - loss: 308.3256 - val_loss: 241.3783\n",
      "Epoch 16/10000000\n",
      " - 0s - loss: 302.5909 - val_loss: 236.3427\n",
      "Epoch 17/10000000\n",
      " - 0s - loss: 296.4475 - val_loss: 231.2525\n",
      "Epoch 18/10000000\n",
      " - 0s - loss: 290.4416 - val_loss: 226.0038\n",
      "Epoch 19/10000000\n",
      " - 0s - loss: 284.2799 - val_loss: 220.7450\n",
      "Epoch 20/10000000\n",
      " - 0s - loss: 277.7946 - val_loss: 215.7167\n",
      "Epoch 21/10000000\n",
      " - 0s - loss: 271.8400 - val_loss: 210.9422\n",
      "Epoch 22/10000000\n",
      " - 0s - loss: 265.8304 - val_loss: 206.7351\n",
      "Epoch 23/10000000\n",
      " - 0s - loss: 260.4738 - val_loss: 203.1385\n",
      "Epoch 24/10000000\n",
      " - 0s - loss: 255.6395 - val_loss: 200.2638\n",
      "Epoch 25/10000000\n",
      " - 0s - loss: 251.9976 - val_loss: 198.2166\n",
      "Epoch 26/10000000\n",
      " - 0s - loss: 248.2025 - val_loss: 197.0029\n",
      "Epoch 27/10000000\n",
      " - 0s - loss: 245.9034 - val_loss: 196.4062\n",
      "Epoch 28/10000000\n",
      " - 0s - loss: 243.8562 - val_loss: 196.4463\n",
      "Epoch 29/10000000\n",
      " - 0s - loss: 242.0419 - val_loss: 196.9243\n",
      "Epoch 30/10000000\n",
      " - 0s - loss: 241.4939 - val_loss: 197.5282\n",
      "Epoch 31/10000000\n",
      " - 0s - loss: 241.0920 - val_loss: 198.0245\n",
      "Epoch 32/10000000\n",
      " - 0s - loss: 240.8747 - val_loss: 198.4490\n",
      "Epoch 33/10000000\n",
      " - 0s - loss: 240.7563 - val_loss: 198.6839\n",
      "Epoch 34/10000000\n",
      " - 0s - loss: 240.5559 - val_loss: 198.7330\n",
      "Epoch 35/10000000\n",
      " - 0s - loss: 240.5655 - val_loss: 198.9215\n",
      "Epoch 36/10000000\n",
      " - 0s - loss: 240.3128 - val_loss: 198.6680\n",
      "Epoch 37/10000000\n",
      " - 0s - loss: 240.1912 - val_loss: 198.4865\n",
      "Epoch 38/10000000\n",
      " - 0s - loss: 240.1707 - val_loss: 198.5354\n",
      "Epoch 39/10000000\n",
      " - 0s - loss: 239.8521 - val_loss: 198.2461\n",
      "Epoch 40/10000000\n",
      " - 0s - loss: 239.7904 - val_loss: 197.8781\n",
      "Epoch 41/10000000\n",
      " - 0s - loss: 239.9121 - val_loss: 197.5384\n",
      "Epoch 42/10000000\n",
      " - 0s - loss: 239.8051 - val_loss: 197.5764\n",
      "Epoch 43/10000000\n",
      " - 0s - loss: 239.6411 - val_loss: 197.8012\n",
      "Epoch 44/10000000\n",
      " - 0s - loss: 239.4158 - val_loss: 198.0564\n",
      "Epoch 45/10000000\n",
      " - 0s - loss: 239.2494 - val_loss: 198.6649\n",
      "Epoch 46/10000000\n",
      " - 0s - loss: 239.0791 - val_loss: 199.2236\n",
      "Epoch 47/10000000\n",
      " - 0s - loss: 239.0726 - val_loss: 199.7865\n",
      "Epoch 48/10000000\n",
      " - 0s - loss: 239.0262 - val_loss: 199.9812\n",
      "Epoch 49/10000000\n",
      " - 0s - loss: 238.9372 - val_loss: 199.9056\n",
      "Epoch 50/10000000\n",
      " - 0s - loss: 238.7978 - val_loss: 199.7509\n",
      "Epoch 51/10000000\n",
      " - 0s - loss: 238.6228 - val_loss: 199.5372\n",
      "Epoch 52/10000000\n",
      " - 0s - loss: 238.4473 - val_loss: 199.2227\n",
      "Epoch 53/10000000\n",
      " - 0s - loss: 238.3821 - val_loss: 198.8013\n",
      "Epoch 54/10000000\n",
      " - 0s - loss: 238.1814 - val_loss: 198.5798\n",
      "Epoch 55/10000000\n",
      " - 0s - loss: 238.0966 - val_loss: 198.1750\n",
      "Epoch 56/10000000\n",
      " - 0s - loss: 238.0117 - val_loss: 198.0500\n",
      "Epoch 57/10000000\n",
      " - 0s - loss: 237.8618 - val_loss: 198.2554\n",
      "Epoch 58/10000000\n",
      " - 0s - loss: 237.7092 - val_loss: 198.5746\n",
      "Epoch 59/10000000\n",
      " - 0s - loss: 237.7295 - val_loss: 198.9153\n",
      "Epoch 60/10000000\n",
      " - 0s - loss: 237.5467 - val_loss: 198.8940\n",
      "Epoch 61/10000000\n",
      " - 0s - loss: 237.4320 - val_loss: 198.6800\n",
      "Epoch 62/10000000\n",
      " - 0s - loss: 237.3223 - val_loss: 198.5539\n",
      "Epoch 63/10000000\n",
      " - 0s - loss: 237.3038 - val_loss: 198.5236\n",
      "Epoch 64/10000000\n",
      " - 0s - loss: 237.1237 - val_loss: 198.9835\n",
      "Epoch 65/10000000\n",
      " - 0s - loss: 237.0398 - val_loss: 199.3556\n",
      "Epoch 66/10000000\n",
      " - 0s - loss: 236.9926 - val_loss: 199.5638\n",
      "Epoch 67/10000000\n",
      " - 0s - loss: 236.9869 - val_loss: 199.8619\n",
      "Epoch 68/10000000\n",
      " - 0s - loss: 236.9322 - val_loss: 199.9018\n",
      "Epoch 69/10000000\n",
      " - 0s - loss: 236.8326 - val_loss: 199.5444\n",
      "Epoch 70/10000000\n",
      " - 0s - loss: 236.6006 - val_loss: 198.9766\n",
      "Epoch 71/10000000\n",
      " - 0s - loss: 236.3486 - val_loss: 198.1425\n",
      "Epoch 72/10000000\n",
      " - 0s - loss: 236.4781 - val_loss: 197.4667\n",
      "Epoch 73/10000000\n",
      " - 0s - loss: 236.3946 - val_loss: 197.2709\n",
      "Epoch 74/10000000\n",
      " - 0s - loss: 236.3165 - val_loss: 197.2895\n",
      "Epoch 75/10000000\n",
      " - 0s - loss: 236.1749 - val_loss: 197.4305\n",
      "Epoch 76/10000000\n",
      " - 0s - loss: 235.9903 - val_loss: 197.5942\n",
      "Epoch 77/10000000\n",
      " - 0s - loss: 236.1782 - val_loss: 198.0923\n",
      "Epoch 78/10000000\n",
      " - 0s - loss: 235.7132 - val_loss: 198.0497\n",
      "Epoch 79/10000000\n",
      " - 0s - loss: 235.6577 - val_loss: 198.0000\n",
      "Epoch 80/10000000\n",
      " - 0s - loss: 235.5127 - val_loss: 197.8643\n",
      "Epoch 81/10000000\n",
      " - 0s - loss: 235.4015 - val_loss: 197.8828\n",
      "Epoch 82/10000000\n",
      " - 0s - loss: 235.3294 - val_loss: 197.9406\n",
      "Epoch 83/10000000\n",
      " - 0s - loss: 235.2051 - val_loss: 198.4744\n",
      "Epoch 84/10000000\n",
      " - 0s - loss: 235.1108 - val_loss: 198.5955\n",
      "Epoch 85/10000000\n",
      " - 0s - loss: 234.9862 - val_loss: 198.3631\n",
      "Epoch 86/10000000\n",
      " - 0s - loss: 234.8161 - val_loss: 198.2144\n",
      "Epoch 87/10000000\n",
      " - 0s - loss: 234.7039 - val_loss: 198.0336\n",
      "Epoch 88/10000000\n",
      " - 0s - loss: 234.5378 - val_loss: 198.0311\n",
      "Epoch 89/10000000\n",
      " - 0s - loss: 234.3574 - val_loss: 198.3667\n",
      "Epoch 90/10000000\n",
      " - 0s - loss: 234.4761 - val_loss: 198.7534\n",
      "Epoch 91/10000000\n",
      " - 0s - loss: 234.3007 - val_loss: 198.6729\n",
      "Epoch 92/10000000\n",
      " - 0s - loss: 234.1115 - val_loss: 198.5859\n",
      "Epoch 93/10000000\n",
      " - 0s - loss: 233.9980 - val_loss: 198.0532\n",
      "Epoch 94/10000000\n",
      " - 0s - loss: 233.7466 - val_loss: 197.6463\n",
      "Epoch 95/10000000\n",
      " - 0s - loss: 233.7787 - val_loss: 197.3472\n",
      "Epoch 96/10000000\n",
      " - 0s - loss: 233.4425 - val_loss: 197.5734\n",
      "Epoch 97/10000000\n",
      " - 0s - loss: 233.3487 - val_loss: 197.8008\n",
      "Epoch 98/10000000\n",
      " - 0s - loss: 233.2328 - val_loss: 197.7281\n",
      "Epoch 99/10000000\n",
      " - 0s - loss: 233.1117 - val_loss: 197.6817\n",
      "Epoch 100/10000000\n",
      " - 0s - loss: 232.9602 - val_loss: 197.9137\n",
      "Epoch 101/10000000\n",
      " - 0s - loss: 232.8739 - val_loss: 197.8880\n",
      "Epoch 102/10000000\n",
      " - 0s - loss: 232.7895 - val_loss: 197.4438\n",
      "Epoch 103/10000000\n",
      " - 0s - loss: 232.7038 - val_loss: 197.4013\n",
      "Epoch 104/10000000\n",
      " - 0s - loss: 232.4241 - val_loss: 198.1633\n",
      "Epoch 105/10000000\n",
      " - 0s - loss: 232.5389 - val_loss: 198.1818\n",
      "Epoch 106/10000000\n",
      " - 0s - loss: 232.2177 - val_loss: 197.4664\n",
      "Epoch 107/10000000\n",
      " - 0s - loss: 231.9705 - val_loss: 196.8382\n",
      "Epoch 108/10000000\n",
      " - 0s - loss: 231.6963 - val_loss: 196.3968\n",
      "Epoch 109/10000000\n",
      " - 0s - loss: 231.5513 - val_loss: 195.9977\n",
      "Epoch 110/10000000\n",
      " - 0s - loss: 231.5665 - val_loss: 195.7375\n",
      "Epoch 111/10000000\n",
      " - 0s - loss: 231.4251 - val_loss: 195.9104\n",
      "Epoch 112/10000000\n",
      " - 0s - loss: 230.9907 - val_loss: 196.2386\n",
      "Epoch 113/10000000\n",
      " - 0s - loss: 230.6764 - val_loss: 196.7135\n",
      "Epoch 114/10000000\n",
      " - 0s - loss: 230.3076 - val_loss: 196.1538\n",
      "Epoch 115/10000000\n",
      " - 0s - loss: 229.7232 - val_loss: 195.9030\n",
      "Epoch 116/10000000\n",
      " - 0s - loss: 229.4335 - val_loss: 195.2312\n",
      "Epoch 117/10000000\n",
      " - 0s - loss: 229.0130 - val_loss: 194.5650\n",
      "Epoch 118/10000000\n",
      " - 0s - loss: 229.5841 - val_loss: 194.3074\n",
      "Epoch 119/10000000\n",
      " - 0s - loss: 230.0447 - val_loss: 194.1773\n",
      "Epoch 120/10000000\n",
      " - 0s - loss: 229.6446 - val_loss: 194.1019\n",
      "Epoch 121/10000000\n",
      " - 0s - loss: 228.8661 - val_loss: 194.3277\n",
      "Epoch 122/10000000\n",
      " - 0s - loss: 228.0737 - val_loss: 195.0831\n",
      "Epoch 123/10000000\n",
      " - 0s - loss: 227.9146 - val_loss: 195.6099\n",
      "Epoch 124/10000000\n",
      " - 0s - loss: 228.3039 - val_loss: 195.4970\n",
      "Epoch 125/10000000\n",
      " - 0s - loss: 227.4324 - val_loss: 194.2471\n",
      "Epoch 126/10000000\n",
      " - 0s - loss: 226.9927 - val_loss: 193.5786\n",
      "Epoch 127/10000000\n",
      " - 0s - loss: 227.0249 - val_loss: 192.9961\n",
      "Epoch 128/10000000\n",
      " - 0s - loss: 226.8555 - val_loss: 192.9233\n",
      "Epoch 129/10000000\n",
      " - 0s - loss: 226.3521 - val_loss: 193.2070\n",
      "Epoch 130/10000000\n",
      " - 0s - loss: 226.2942 - val_loss: 194.4982\n",
      "Epoch 131/10000000\n",
      " - 0s - loss: 226.1386 - val_loss: 195.4029\n",
      "Epoch 132/10000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 0s - loss: 225.8677 - val_loss: 194.0932\n",
      "Epoch 133/10000000\n",
      " - 0s - loss: 225.0746 - val_loss: 192.8357\n",
      "Epoch 134/10000000\n",
      " - 0s - loss: 224.8184 - val_loss: 191.6137\n",
      "Epoch 135/10000000\n",
      " - 0s - loss: 224.4828 - val_loss: 191.6492\n",
      "Epoch 136/10000000\n",
      " - 0s - loss: 223.8026 - val_loss: 192.1965\n",
      "Epoch 137/10000000\n",
      " - 0s - loss: 223.7140 - val_loss: 193.6813\n",
      "Epoch 138/10000000\n",
      " - 0s - loss: 224.0164 - val_loss: 193.0983\n",
      "Epoch 139/10000000\n",
      " - 0s - loss: 223.2894 - val_loss: 192.5623\n",
      "Epoch 140/10000000\n",
      " - 0s - loss: 222.5569 - val_loss: 190.9665\n",
      "Epoch 141/10000000\n",
      " - 0s - loss: 221.9068 - val_loss: 189.9485\n",
      "Epoch 142/10000000\n",
      " - 0s - loss: 221.5390 - val_loss: 189.2122\n",
      "Epoch 143/10000000\n",
      " - 0s - loss: 221.4248 - val_loss: 188.5644\n",
      "Epoch 144/10000000\n",
      " - 0s - loss: 221.0609 - val_loss: 188.0770\n",
      "Epoch 145/10000000\n",
      " - 0s - loss: 220.7368 - val_loss: 187.8320\n",
      "Epoch 146/10000000\n",
      " - 0s - loss: 220.1484 - val_loss: 187.6898\n",
      "Epoch 147/10000000\n",
      " - 0s - loss: 219.4844 - val_loss: 187.3777\n",
      "Epoch 148/10000000\n",
      " - 0s - loss: 219.0358 - val_loss: 187.2445\n",
      "Epoch 149/10000000\n",
      " - 0s - loss: 218.5353 - val_loss: 186.8537\n",
      "Epoch 150/10000000\n",
      " - 0s - loss: 217.9757 - val_loss: 186.5437\n",
      "Epoch 151/10000000\n",
      " - 0s - loss: 217.4511 - val_loss: 186.0903\n",
      "Epoch 152/10000000\n",
      " - 0s - loss: 216.9645 - val_loss: 184.9469\n",
      "Epoch 153/10000000\n",
      " - 0s - loss: 216.3938 - val_loss: 184.5468\n",
      "Epoch 154/10000000\n",
      " - 0s - loss: 215.6852 - val_loss: 184.9159\n",
      "Epoch 155/10000000\n",
      " - 0s - loss: 215.2845 - val_loss: 186.8736\n",
      "Epoch 156/10000000\n",
      " - 0s - loss: 216.6804 - val_loss: 187.6324\n",
      "Epoch 157/10000000\n",
      " - 0s - loss: 214.9999 - val_loss: 183.1812\n",
      "Epoch 158/10000000\n",
      " - 0s - loss: 212.9916 - val_loss: 180.8576\n",
      "Epoch 159/10000000\n",
      " - 0s - loss: 213.1575 - val_loss: 180.1301\n",
      "Epoch 160/10000000\n",
      " - 0s - loss: 213.7664 - val_loss: 179.4552\n",
      "Epoch 161/10000000\n",
      " - 0s - loss: 212.8575 - val_loss: 178.8515\n",
      "Epoch 162/10000000\n",
      " - 0s - loss: 211.3917 - val_loss: 178.7752\n",
      "Epoch 163/10000000\n",
      " - 0s - loss: 209.6184 - val_loss: 180.3216\n",
      "Epoch 164/10000000\n",
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      "Epoch 165/10000000\n",
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      "Epoch 166/10000000\n",
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      "Epoch 167/10000000\n",
      " - 0s - loss: 207.2842 - val_loss: 175.1391\n",
      "Epoch 168/10000000\n",
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      "Epoch 169/10000000\n",
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      "Epoch 170/10000000\n",
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      "Epoch 171/10000000\n",
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      "Epoch 172/10000000\n",
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      "Epoch 173/10000000\n",
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      "Epoch 174/10000000\n",
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      "Epoch 175/10000000\n",
      " - 0s - loss: 199.5050 - val_loss: 168.3026\n",
      "Epoch 176/10000000\n",
      " - 0s - loss: 199.2813 - val_loss: 167.4313\n",
      "Epoch 177/10000000\n",
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      "Epoch 178/10000000\n",
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      "Epoch 180/10000000\n",
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      "Epoch 181/10000000\n",
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      "Epoch 182/10000000\n",
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      "Epoch 183/10000000\n",
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      "Epoch 185/10000000\n",
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      "Epoch 186/10000000\n",
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      "Epoch 187/10000000\n",
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      "Epoch 189/10000000\n",
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      "Epoch 190/10000000\n",
      " - 0s - loss: 180.2125 - val_loss: 152.1032\n",
      "Epoch 191/10000000\n",
      " - 0s - loss: 178.1442 - val_loss: 149.4443\n",
      "Epoch 192/10000000\n",
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      "Epoch 193/10000000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1210/10000000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1484/10000000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1621/10000000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1758/10000000\n",
      " - 0s - loss: 2.0416 - val_loss: 2.2114\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1895/10000000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2032/10000000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2169/10000000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2580/10000000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " - 0s - loss: 1.2234 - val_loss: 1.5814\n",
      "Epoch 2839/10000000\n",
      " - 0s - loss: 1.2464 - val_loss: 1.5688\n",
      "Epoch 2840/10000000\n",
      " - 0s - loss: 1.2262 - val_loss: 1.6295\n",
      "Epoch 2841/10000000\n",
      " - 0s - loss: 1.2454 - val_loss: 1.7173\n",
      "Epoch 2842/10000000\n",
      " - 0s - loss: 1.3148 - val_loss: 1.7689\n",
      "Epoch 2843/10000000\n",
      " - 0s - loss: 1.3170 - val_loss: 1.6865\n",
      "Epoch 2844/10000000\n",
      " - 0s - loss: 1.2286 - val_loss: 1.6372\n",
      "Epoch 2845/10000000\n",
      " - 0s - loss: 1.2354 - val_loss: 1.6543\n",
      "Epoch 2846/10000000\n",
      " - 0s - loss: 1.2809 - val_loss: 1.7583\n",
      "Epoch 2847/10000000\n",
      " - 0s - loss: 1.4116 - val_loss: 1.6750\n",
      "Epoch 2848/10000000\n",
      " - 0s - loss: 1.3596 - val_loss: 1.7114\n",
      "Epoch 2849/10000000\n",
      " - 0s - loss: 1.3105 - val_loss: 1.6621\n",
      "Epoch 2850/10000000\n",
      " - 0s - loss: 1.2576 - val_loss: 1.6816\n",
      "Epoch 2851/10000000\n",
      " - 0s - loss: 1.2970 - val_loss: 1.6773\n",
      "Epoch 2852/10000000\n",
      " - 0s - loss: 1.3091 - val_loss: 1.7283\n",
      "Epoch 2853/10000000\n",
      " - 0s - loss: 1.4163 - val_loss: 1.8677\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2854/10000000\n",
      " - 0s - loss: 1.5100 - val_loss: 1.6936\n",
      "Epoch 2855/10000000\n",
      " - 0s - loss: 1.2755 - val_loss: 1.5986\n",
      "Epoch 2856/10000000\n",
      " - 0s - loss: 1.2047 - val_loss: 1.7891\n",
      "Epoch 2857/10000000\n",
      " - 0s - loss: 1.3790 - val_loss: 2.1319\n",
      "Epoch 2858/10000000\n",
      " - 0s - loss: 1.7727 - val_loss: 2.3161\n",
      "Epoch 2859/10000000\n",
      " - 0s - loss: 2.1040 - val_loss: 2.7135\n",
      "Epoch 2860/10000000\n",
      " - 0s - loss: 2.5121 - val_loss: 2.0968\n",
      "Epoch 2861/10000000\n",
      " - 0s - loss: 1.9862 - val_loss: 1.7401\n",
      "Epoch 2862/10000000\n",
      " - 0s - loss: 1.4731 - val_loss: 1.7472\n",
      "Epoch 2863/10000000\n",
      " - 0s - loss: 1.4607 - val_loss: 1.8978\n",
      "Epoch 2864/10000000\n",
      " - 0s - loss: 2.1380 - val_loss: 1.9579\n",
      "Epoch 2865/10000000\n",
      " - 0s - loss: 2.2277 - val_loss: 1.9058\n",
      "Epoch 2866/10000000\n",
      " - 0s - loss: 1.9395 - val_loss: 2.0367\n",
      "Epoch 2867/10000000\n",
      " - 0s - loss: 2.1432 - val_loss: 1.7846\n",
      "Epoch 2868/10000000\n",
      " - 0s - loss: 1.5815 - val_loss: 1.5813\n",
      "Epoch 2869/10000000\n",
      " - 0s - loss: 1.3837 - val_loss: 1.6219\n",
      "Epoch 2870/10000000\n",
      " - 0s - loss: 1.5442 - val_loss: 1.6348\n",
      "Epoch 2871/10000000\n",
      " - 0s - loss: 1.3181 - val_loss: 1.7539\n",
      "Epoch 2872/10000000\n",
      " - 0s - loss: 1.2961 - val_loss: 1.7880\n",
      "Epoch 2873/10000000\n",
      " - 0s - loss: 1.2647 - val_loss: 1.6432\n",
      "Epoch 2874/10000000\n",
      " - 0s - loss: 1.2804 - val_loss: 2.1699\n",
      "Epoch 2875/10000000\n",
      " - 0s - loss: 2.1029 - val_loss: 2.1830\n",
      "Epoch 2876/10000000\n",
      " - 0s - loss: 1.7057 - val_loss: 1.8764\n",
      "Epoch 2877/10000000\n",
      " - 0s - loss: 1.5514 - val_loss: 1.7045\n",
      "Epoch 2878/10000000\n",
      " - 0s - loss: 1.3338 - val_loss: 1.6684\n",
      "Epoch 2879/10000000\n",
      " - 0s - loss: 1.3123 - val_loss: 1.6467\n",
      "Epoch 2880/10000000\n",
      " - 0s - loss: 1.2818 - val_loss: 1.6616\n",
      "Epoch 2881/10000000\n",
      " - 0s - loss: 1.2472 - val_loss: 1.6100\n",
      "Epoch 2882/10000000\n",
      " - 0s - loss: 1.2557 - val_loss: 1.6055\n",
      "Epoch 2883/10000000\n",
      " - 0s - loss: 1.4362 - val_loss: 2.0891\n",
      "Epoch 2884/10000000\n",
      " - 0s - loss: 2.0429 - val_loss: 1.9522\n",
      "Epoch 2885/10000000\n",
      " - 0s - loss: 1.7187 - val_loss: 1.6589\n",
      "Epoch 2886/10000000\n",
      " - 0s - loss: 1.4107 - val_loss: 1.6713\n",
      "Epoch 2887/10000000\n",
      " - 0s - loss: 1.2293 - val_loss: 1.7911\n",
      "Epoch 2888/10000000\n",
      " - 0s - loss: 1.3573 - val_loss: 1.6730\n",
      "Epoch 2889/10000000\n",
      " - 0s - loss: 1.2317 - val_loss: 1.7985\n",
      "Epoch 2890/10000000\n",
      " - 0s - loss: 1.2874 - val_loss: 1.7737\n",
      "Epoch 2891/10000000\n",
      " - 0s - loss: 1.2967 - val_loss: 1.7868\n",
      "Epoch 2892/10000000\n",
      " - 0s - loss: 1.3952 - val_loss: 1.6812\n",
      "Epoch 2893/10000000\n",
      " - 0s - loss: 1.2081 - val_loss: 1.6854\n",
      "Epoch 2894/10000000\n",
      " - 0s - loss: 1.2446 - val_loss: 1.6388\n",
      "Epoch 2895/10000000\n",
      " - 0s - loss: 1.2554 - val_loss: 1.6857\n",
      "Epoch 2896/10000000\n",
      " - 0s - loss: 1.4464 - val_loss: 1.6381\n",
      "Epoch 2897/10000000\n",
      " - 0s - loss: 1.3243 - val_loss: 1.5752\n",
      "Epoch 2898/10000000\n",
      " - 0s - loss: 1.2213 - val_loss: 1.6341\n",
      "Epoch 2899/10000000\n",
      " - 0s - loss: 1.2571 - val_loss: 1.5832\n",
      "Epoch 2900/10000000\n",
      " - 0s - loss: 1.3243 - val_loss: 1.5670\n",
      "Epoch 2901/10000000\n",
      " - 0s - loss: 1.2035 - val_loss: 1.6549\n",
      "Epoch 2902/10000000\n",
      " - 0s - loss: 1.2669 - val_loss: 1.6388\n",
      "Epoch 2903/10000000\n",
      " - 0s - loss: 1.2295 - val_loss: 1.6497\n",
      "Epoch 2904/10000000\n",
      " - 0s - loss: 1.2436 - val_loss: 1.6380\n",
      "Epoch 2905/10000000\n",
      " - 0s - loss: 1.2798 - val_loss: 1.5797\n",
      "Epoch 2906/10000000\n",
      " - 0s - loss: 1.2648 - val_loss: 1.6013\n",
      "Epoch 2907/10000000\n",
      " - 0s - loss: 1.2643 - val_loss: 1.5884\n",
      "Epoch 2908/10000000\n",
      " - 0s - loss: 1.2261 - val_loss: 1.5533\n",
      "Epoch 2909/10000000\n",
      " - 0s - loss: 1.2886 - val_loss: 1.5651\n",
      "Epoch 2910/10000000\n",
      " - 0s - loss: 1.2317 - val_loss: 1.6152\n",
      "Epoch 2911/10000000\n",
      " - 0s - loss: 1.3004 - val_loss: 1.7180\n",
      "Epoch 2912/10000000\n",
      " - 0s - loss: 1.4056 - val_loss: 1.7790\n",
      "Epoch 2913/10000000\n",
      " - 0s - loss: 1.4889 - val_loss: 2.1128\n",
      "Epoch 2914/10000000\n",
      " - 0s - loss: 2.3980 - val_loss: 2.2963\n",
      "Epoch 2915/10000000\n",
      " - 0s - loss: 2.3543 - val_loss: 2.3428\n",
      "Epoch 2916/10000000\n",
      " - 0s - loss: 2.3619 - val_loss: 1.8337\n",
      "Epoch 2917/10000000\n",
      " - 0s - loss: 1.8441 - val_loss: 1.6354\n",
      "Epoch 2918/10000000\n",
      " - 0s - loss: 1.4318 - val_loss: 1.7877\n",
      "Epoch 2919/10000000\n",
      " - 0s - loss: 1.3174 - val_loss: 1.7808\n",
      "Epoch 2920/10000000\n",
      " - 0s - loss: 1.2819 - val_loss: 2.2757\n",
      "Epoch 2921/10000000\n",
      " - 0s - loss: 2.0185 - val_loss: 2.4325\n",
      "Epoch 2922/10000000\n",
      " - 0s - loss: 2.3058 - val_loss: 1.8756\n",
      "Epoch 2923/10000000\n",
      " - 0s - loss: 1.4975 - val_loss: 1.7289\n",
      "Epoch 2924/10000000\n",
      " - 0s - loss: 1.2469 - val_loss: 1.6524\n",
      "Epoch 2925/10000000\n",
      " - 0s - loss: 1.2286 - val_loss: 1.6308\n",
      "Epoch 2926/10000000\n",
      " - 0s - loss: 1.1900 - val_loss: 1.6834\n",
      "Epoch 2927/10000000\n",
      " - 0s - loss: 1.2365 - val_loss: 1.7037\n",
      "Epoch 2928/10000000\n",
      " - 0s - loss: 1.2640 - val_loss: 1.8274\n",
      "Epoch 2929/10000000\n",
      " - 0s - loss: 1.4737 - val_loss: 2.2565\n",
      "Epoch 2930/10000000\n",
      " - 0s - loss: 2.1426 - val_loss: 2.6056\n",
      "Epoch 2931/10000000\n",
      " - 0s - loss: 2.5200 - val_loss: 1.9239\n",
      "Epoch 2932/10000000\n",
      " - 0s - loss: 1.5277 - val_loss: 1.7396\n",
      "Epoch 2933/10000000\n",
      " - 0s - loss: 1.2773 - val_loss: 1.6526\n",
      "Epoch 02933: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Create model\n",
    "model = Sequential()\n",
    "model.add(Dense(128, activation=\"relu\", input_dim=6))\n",
    "model.add(Dense(32, activation=\"relu\"))\n",
    "model.add(Dense(8, activation=\"relu\"))\n",
    "# Since the regression is performed, a Dense layer containing a single neuron with a linear activation function.\n",
    "# Typically ReLu-based activation are used but since it is performed regression, it is needed a linear activation.\n",
    "model.add(Dense(1, activation=\"linear\"))\n",
    "\n",
    "# Compile model: The model is initialized with the Adam optimizer and then it is compiled.\n",
    "model.compile(loss='mean_squared_error', optimizer=Adam(lr=1e-3, decay=1e-3 / 200))\n",
    "\n",
    "# Patient early stopping\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=200)\n",
    "\n",
    "# Fit the model\n",
    "history = model.fit(X1, Y1, validation_data=(X2, Y2), epochs=10000000, batch_size=100, verbose=2, callbacks=[es])\n",
    "\n",
    "# Calculate predictions\n",
    "PredTestSet = model.predict(X1)\n",
    "PredValSet = model.predict(X2)\n",
    "\n",
    "# Save predictions\n",
    "numpy.savetxt(\"trainresults.csv\", PredTestSet, delimiter=\",\")\n",
    "numpy.savetxt(\"valresults.csv\", PredValSet, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yAMjNaFJNQkSkHcm843oocI+ZZRAko4fd/SkzWwD81cx+DrwL3Bnufydwn5mVApuB85IWWWaYJCKN1GgIrIhIXElLEu4+Fzi0jfIPCfondi6vBb6crHh2kBkDIMcaqVWSEBGJq2/ecZ2ZDUAs0khdYzPNzclp1RIRSXd9NEmENYlIMBJXw2BFRNrWN5NE2HEdox6A2gaNcBIRaUvfTBJhTSJmDQDqvBYRiaOPJolsiETJaa4GdEOdiEg8fTNJmEFOIbGmbQAa4SQiEkffTBIAsUJijVsBJQkRkXg6TBJmdoOZ9TezqJm9ZGYbzOyC7gguqXIKyWpoSRLquBYRaUsiNYnPu/tW4HRgObA3cGUyg+oWsUKiYZJQx7WISNsSSRItd2WfBvzN3SuSGE/3ySkkWq8kISLSnkQey/GUmS0CaoD/NLNioDa5YXWDWCGRuiDfqU9CRKRtHdYk3H0K8Glggrs3AFUEs8ilt5wgSRjNShIiInEk0nH9ZaDB3ZvM7CfA/cCwpEeWbLFCDCefGt0nISISRyJ9Ele7+zYzOxb4HMEjvW9NbljdIKcQgP5WpdFNIiJxJJIkWv7MPg243d2fBrKSF1I3iQVJYlCkRh3XIiJxJJIkVpnZ/wFfAaabWXaCx/VsYU2iKFqjPgkRkTgS+WV/LvAcMDGcq3ogCdwnYWYjzOwVM1tgZvPN7Iqw/FozW2Vmc8LXqa2OmWpmpWa22MwmdvKaEhMrAGBQhpKEiEg8HQ6BdfdqM/sAmBj+4n7d3Z9P4NyNwA/d/R0zywdmm9kL4bab3P03rXc2swMIpiw9kKBj/EUz28fdk/MbPJoLQEFGPevUcS0i0qZERjddATwADA5f95vZ5R0d5+5r3P2dcHkbsBAY3s4hZwJ/dfc6d18GlNLGNKddJqsfAIXRBrbVNiTtY0RE0lkizU0XA0e6+zXufg1wFHDJrnyImY0imO96Zlj0XTOba2Z3mdmAsGw4sLLVYWW0kVTMbLKZzTKzWRs2bNiVMHYU1iQGZDZSXq0kISLSlkSShPHxCCfCZUv0A8wsD/g78L3wGVC3AmOA8cAa4LcJRwu4++3uPsHdJxQXF+/KoTsKk0RhtIHy6vrOn0dEpBdL5LEcfwZmmtlj4fpZwF2JnNzMogQJ4gF3fxTA3de12n4H8FS4ugoY0erwkrAsOTIyISOL/hn1bK5UkhARaUsij+W4EbgI2By+LnL3mzo6zsyM4Ma7heE5WsqHttrtbGBeuPwEcJ6ZZZvZaGAs8FaiF9Ip0VwGRBvZVtdIWXl1Uj9KRCQdJVKTIOyAfqdl3cw+cveRHRx2DPB14H0zmxOW/TfwVTMbDzjBo8cvDT9jvpk9DCwgGBl1WdJGNrXI6kdJHmRlRJh402t854S9+dZnRpOdmZHUjxURSRcJJYk2dNgn4e5vxNlvejvH/AL4RSdj2nXRXAoy6nn420fzm+cW8+vnFvPwrJV8+7gxnH7wUPJj0W4LRUSkJ+rsndPepVGkSlYu1FczfkQh93/rSO79jyPIzoww9dH3Ofp/X+a+GctpbNJznUSk74pbkzCzH8TbBOQlJ5xuFu0HDR/3RXx2n2Ke3fuzvF66keufWcTVj8/n0XdX8ZsvH8KY4t5xySIiu6K9mkR+nFce8Lvkh9YNsnKhvmqHokjEOG6fYp787jFce8YBlK6v5JSbX+fZeWtTFKSISOrErUm4+3XdGUhKRHOhoazNTZkZEb55zGhOO3gYZ/3hX3z7/tlcffoBXHzs6G4OUkQkddL/aa67I6sf1Lc/9LU4P5tbzj8UgJ89tYDNVbqnQkT6jr6dJKK50FDV4W6HjRzAz84aB8APHp7Twd4iIr1H304S4eimRHz9qD254qSxvLp4A4++03YTlYhIb5PIU2Czzex8M/tvM7um5dUdwSVdNBcaa6A5sWGu3z5uDAB3vL4M994xClhEpD2J1CQeJ3iMdyNQ1eqV/sKH/LUeBtuenKwMrvvCgSxcs5UZH25KYmAiIj1DIndcl7j7pKRHkgrhnBI0VEN2YvdBfOXwEdzw7CLOv2Mmpb84hcyMvt1iJyK9WyK/4f5tZgclPZJUaKlJ1CdeMYpFM8jKDP7Z7n9zRTKiEhHpMRJJEscSTD26OJwo6H0zm5vswLpFdn7wXl+5S4fd+c3DAbj2yQVdHZGISI+SSHPTKUmPIlVakkTdtl06bHxJYRKCERHpeRKZT2IFUAicEb4Kw7L0l90/eN/FJBGJGF89InhS+rKNvaMPX0SkLYkMgb0CeAAYHL7uN7PLkx1Yt+hkTQLgjEOCuZNmryjvyohERHqURPokLgaOdPdr3P0a4Cjgko4OMrMRZvaKmS0ws/lhssHMBprZC2a2NHwfEJabmU0zs9Kw7+Ow3bmwhGxPElt3+dAjRw8iNyuDeasqujgoEZGeI5EkYUDrGeKaSGDSIYL7Kn7o7gcQJJbLzOwAYArwkruPBV4K1yHo+xgbviYDtyZ0BbtjN2oSGRHjwGH9eV9JQkR6sUSSxJ+BmWZ2rZldC7xJMHd1u9x9TTjtKe6+DVgIDCe4Me+ecLd7gLPC5TOBez3wJlC403zYXS+rH2BBkmishzVzoakB1s1P6PCDhhcyf3WFJiYSkV6rw9FN7n6jmb1KMBQW4CJ3f3dXPsTMRgGHAjOBIe6+Jty0FhgSLg8HVrY6rCwsW9OqDDObTFDTYOTIjqbZ7jAwyCmE134Nr/8WvNUv+/1Oh+YmKNobRn4a9jv1E4cfXFLAXf9qpnRDJfvt0X/3YhER6YHam5muv7tvNbOBwPLw1bJtoLtvTuQDzCwP+DvwvfB827e5u5vZLj0Eyd1vB24HmDBhwu4/QKkm7Hj2nWoDi54K3pcA/74FPv3/4Lirdrgz+6CSAgDmrqxQkhCRXqm9msSDwOnAbHac09rC9b06OrmZRQkSxAPu/mhYvM7Mhrr7mrA5aX1YvgoY0erwkrAsuU6/GRY8Dp/+LpQcDhlZsOw1qN4MhSNh5m2w8An497TgNeJIuPh5AEYP6kdediZzV23h3MNHdPBBIiLpp72Z6U4P3zs1FZsFVYY7gYXufmOrTU8AFwLXh++Ptyr/rpn9FTgSqGjVLJU8Ey4KXq3tM/Hj5VHHwAcvw31nB+srZ8Lse+BTFxKJGOOG9+f9Vbs+OkpEJB0kcp/ES4mUteEY4OvAiWY2J3ydSpAcTjazpcDnwnWA6cCHQClwB/CdxC6hG4w5Ea5qdf9gS1MUcMiIQuavqqCqrjEFgYmIJFd7fRIxIBcoCu9laOlM6E/Qodwud3+D+ENlT2pjfwcu6+i8KZNTCFNXwbTxsHUNbP4QBu7FQcMLaGx2Ptpczf5D1S8hIr1LezWJSwn6I/YL31tejwO/T35oPVB2Hhw/Fda9D9MOhSXPMWJA8CTZsvKaFAcnItL14iYJd/9d2B/xI3ffy91Hh69D3L1vJgmACf/x8fLmDykZkAPAys2JTVwkIpJOEnnA3y1mNs7MzjWzb7S8uiO4HsksGBEF8OwUBmY1kZuVoZqEiPRKiXRc/xS4JXydANwAfCHJcfVs4760fdE2lVIyIIeV5apJiEjvk8hjOc4h6Ghe6+4XAYcABUmNqqdreeYTQE05VXVNvLBgHUHfu4hI75FIkqhx92ag0cz6E9z81rfvHDOD028KlivXMaBfNFjUMFgR6WUSSRKzzKyQ4N6F2cA7wIykRpUOxp0TvM97lEs+E9x8vm5rbQoDEhHpeol0XH/H3be4+23AycCFYbNT39bS5LTkGYbkZwOwtqIuhQGJiHS9uEnCzA7b+QUMBDK7ZUKgns4MJv4vAMOygk7rtapJiEgv094D/n4bvseACcB7BHdQHwzMAo5ObmhpYMAoAIYtuQ8Yr+YmEel12ruZ7gR3P4FgPofD3H2Cu3+KYF6I5D+dNR0MOQCAzNl3UpgbZU2F7pUQkd6lw0mHgH3d/f2WFXefZ2b7JzGm9DFgFBTtA/lD2aM8xtoK1SREpHdJZHTTXDP7k5kdH77uAOYmO7C0kVsEy/7JIf3KWb1FSUJEepdEksRFwHzgivC1ICwTgLK3ATi79h9qbhKRXieROa5rgZvCl+zsyEthxu/JjzZRXt1ATX0TOVkZqY5KRKRLtDcE9uHw/X0zm7vzq/tC7OE+/3MAYtkxQMNgRaR3aa8mcUX4fnpnTmxmd4XHrnf3cWHZtcAlwIZwt/929+nhtqnAxUAT8P/c/bnOfG63M4M9DmJg4zoA1mypYXRRvxQHJSLSNdqb43pN+L4i3j4duJtgcqJ7dyq/yd1/07rAzA4AzgMOBIYBL5rZPu7e1MnP7nYDVr0KXMJqjXASkV6kveambWa2tY3XNjPb2tGJ3f01YHOCcZwJ/NXd69x9GcE810ckeGzqrQ1GCB9gK1irzmsR6UXau5ku3937t/HKd/fdmcz5u2G/xl3h3NkQzJm9stU+ZcSZR9vMJpvZLDObtWHDhrZ2SZnBOa6ahIj0KokMgQXAzAab2ciWVyc/71ZgDDCe4E7u37a/+ye5++3h3d8TiouLOxlGF/viHQCMyG3UDXUi0qskMjPdF8xsKbAM+CewHHimMx/m7uvcvSmcn+IOPm5SWsWOc1SUkE6P/hg6HoBT/DXWb1OSEJHeI5GaxM+Ao4Al7j6aYJa6NzvzYWY2tNXq2cC8cPkJ4Dwzyzaz0cBY4K3OfEZKFAQtYwfWzdXjwkWkV0nk2U0N7r7JzCJmFnH3V8zs5o4OMrO/AMcDRWZWBvwUON7MxgNOUCO5FMDd54f3ZSwAGoHL0mlkE1n9oGhf6qub2VReR0NTM9GMhFvyRER6rESSxBYzywNeAx4ws/VAVUcHuftX2yi+s539fwH8IoF4eqaNiykG3GFjZR1DC3JSHZGIyG5L5M/dM4Ea4PvAs8AHwBnJDCqdZdHAuq1qchKR3qG9+yT+YGbHuHtV2Nnc6O73uPs0d9/UnUGmhVOD+wP7U63Jh0Sk12ivJrEE+I2ZLTezG8zs0O4KKi3FCgDYy1YrSYhIr9HezXS/c/ejgeOATcBdZrbIzH5qZvt0W4Tpor4SgHuzrleSEJFeo8M+CXdf4e6/cvdDga8CZwELkx5ZutnzWADmRfZVn4SI9BqJ3EyXaWZnmNkDBDfRLQa+mPTI0k1xULma4PN4ZHaZbqoTkV6hvY7rk8PHfZcRPN77aWCMu5/n7o93V4Dp6tv3zU51CCIiu629+ySmAg8CP3T38m6Kp9fYUKkmJxFJf+3NJ3FidwbSmxxhC1nROD7VYYiI7LZE7riWXTTEyjlg3NCOdxQR6eH0gKGulBk8iuM7WU/T3FSf4mBERHafkkRXOu9+APZnGfuvfSrFwYiI7D4lia7U/+PJ9JrqNY2piKQ/JYmulNVv++KS9ZUpDEREpGsoSXSlgo8n13OMipqGFAYjIrL7kpYkzOwuM1tvZvNalQ00sxfMbGn4PiAsNzObZmalZjbXzA5LVlxJZbbD6gbddS0iaS6ZNYm7gUk7lU0BXnL3scBL4TrAKQRTlo4FJgO3JjGubvPR5upUhyAisluSliTc/TVg807FZwL3hMv3EDwssKX8Xg+8CRTuNB92Wlq2UUlCRNJbd/dJDHH3NeHyWmBIuDwcWNlqv7Kw7BPMbLKZzTKzWRs2bEhepLvpZ9G7WVuhEU4ikt5S1nHt7g54J4673d0nuPuE4uLiJES2m0Z/dvvi6gr1SYhIeuvuJLGupRkpfF8flq8CRrTaryQsSz+n3bR9cd6qihQGIiKy+7o7STwBXBguXwg83qr8G+Eop6OAilbNUumlaO/tiys2VXPfmytSGIyIyO5J5hDYvwAzgH3NrMzMLgauB042s6XA58J1gOnAh0ApcAfwnWTF1Z2iNHL1P+Z1vKOISA+VtKfAuvtX42w6qY19HbgsWbGkytcyXuTupkm8uGAdnztgSMcHiIj0MLrjOolOyXgLgG/dO4sZH2xKcTQiIrtOSSIZDj4PgMMjS7YXfbS5ilteWkp9Y3OqohIR2WVKEslw8v8AEKGZMyNvAHDV398m0fYUAAAS50lEQVTnty8s4X51ZItIGlGSSIZ+H9+/8busP7I8dj6jbQ3D2cCCNVtTGJiIyK5RkkiGyCf/Wf+RdTX/il3Bk7M/TEFAIiKdoySRLIecv8NqgQXPccpG05qKSPpQkkiW029ss/jgyLJuDkREpPOUJJIlmrND30SLH2fez5J121IQkIjIrlOSSKYrSz9RtH9kJZ+/6bUUBCMisuuUJJJtykdtFm+pVt+EiPR8ShLJFiuAayvgs/+1Q/H4/3mBt5YFczKVrt/Gq4vXM3vFZmavKE9FlCIibUras5tkJyf+GF67IVxxwDj3/2bssIsR3I297Pozujc2EZE4VJNIgQ/H/p5BfHKuiWWxC3gw+kteWLCObbUNKYhMRGRHqkmkQGTlDGbHZrBg8kpOnfb6DtuOzljAqHtnAXD+kSP55dkHpSJEERFANYnudfzUHVYPWPsPln9lCx/+aD+OL67aXn5m5A2iNPLgzI94eNZKqusbuztSEREALJjKIT1NmDDBZ82aleowds21BQnvelTtLaxlEAB/OP8w3v2onJqGJr5/8j4U5WUnK0IR6eXMbLa7T0ho31QkCTNbDmwDmoBGd59gZgOBh4BRwHLgXHdvd6hPWiYJd7hhNNQkNorppLpf059q3vWx28vysjOZd93EVqd0NlXVK3GISEJ2JUmksrnpBHcf3yrQKcBL7j4WeClc733M4KrlcPUm2L/jUUwvZV/JY9k/JYMm+lFDP2qorGtk1JSnWbk5eB7UL55eyISfv7h9XUSkq6SyJjHB3Te2KlsMHO/ua8xsKPCqu+/b3nnSsiYRz9y/waPfSmjXL9Zdyzu+z/b1MbaKyzMf48qGb7P0+jOTFaGI9BLp0Ny0DCgnuGHg/9z9djPb4u6F4XYDylvWdzp2MjAZYOTIkZ9asaKXTeLT3ARbPoJp49vd7YXcU7lk8wWMsHW8nv19AKY1nsW8fS7nj187jMyMCFtrG6iqa2RoQU53RC4iaSIdksRwd19lZoOBF4DLgSdaJwUzK3f3Ae2dp1fVJOJZ+CQ8dEHCu4+qfZCfnzWO2SvKeezdVQAsv/60ZEUnImloV5JESu6TcPdV4ft6M3sMOAJYZ2ZDWzU3rU9FbD3O/mcEj/VoaoDZd8P0H7W7+372ET/5R7CcSSNNRHhu/lomHrhH8mMVkV6n2zuuzayfmeW3LAOfB+YBTwAXhrtdCDze3bH1aBlROOKSIGF8fwEMO7TN3Z7NnsKXIsFTZktj3+D30Wlcet9sVm2p6c5oRaSX6PbmJjPbC3gsXM0EHnT3X5jZIOBhYCSwgmAI7Ob2ztUnmpva4w5zH4bHJre72/NNn2Jyww8xgymT9mP5pir+94sHd1OQItLT9Pg+ia7S55NEa7UVcP3IuJv/1HgKNzSex0hbR6XncPnZx/G1I/fsxgBFpKdQkujLGuvhn9fD679td7dRtQ9uX37tyhMYOSg32ZGJSA+RLjfTSTJkZsFJ1wR9F5e9HXe3iZG3WB47n+Wx8znt10/zzT+/xT/eXcWclVu6MVgR6elUk+gLqjbCr8fE3TyreR9ubTyDjV7AcZG59J84lQOGFbKhso4zxw/vxkBFpDv0+CGw0s36FQU1i+amYBjt0z/YYfOEyBLuzPq4eeq252s5v/F8ACUJkT5ONYm+qmoT/HqvuJvnNo/m0abPcHfTJAAW/WwS1z05n0s+sxd7Fed1V5QikgTquJbENdTAk9+DuX+Nu8uc5jF8o34Kv43exo8aLuW967/SjQGKSFdTkpDO2boabty/w91G1T7IT07bn4uPHU3wmC0RSSca3SSd039Y0Hdx9SY44Sdxd3si68dUPvczxk99mFFTnuZrf3oTd+eJWR9Q19jUjQGLSLKpJiHtqymH+8+BVfH/nac0fIsqj3FL1u+5qP5K/vzL+AlGRFJPzU2SHPVV8NDX4YOX2t1tdO39DGYL6ykkRj2fP2Q0i+fO5GuHDuSCL5+rJiqRFNMQWEmOrH7w9UeD5fpqeP03bd7ZvSy206PNFwPZwAK4dVopky//bzIiShQi6UA1Cekaq+fA7cclvPtX639MnUcZF1nGNs9l6tRrGZwfa3PfrTX1lK6r4LBRxV0VrUifpuYmSa26Slj4BEy/EuorEz7s9si5nMjb1MQGM+5Hz2KRCM3NzopfforRjR/QcOJ1RI+6JKjRiEinKUlIz+IedID/5TxYOXO3TrUto5D8qz85ZW3TijfJyCuGQfEfPyIiAfVJSM9iBrkD4eLndyyv3gxr3sP/diFWW5HQqfKbtsC1BZ8ozwjfHxz1S6J77M/wQfn0H7wnV97+GPdMuZC87Exyszr+cd9WVU1u+WIqVy8kNvY4sgcEjyXZVFlH/5wo0aaaXa/J1FcFiTJbd6pL+ulxNQkzmwT8juD//Z/c/fp4+6om0UtVboD5j8HMW2Hzh0n7mBmRQ6mIlTCuagbP7vUT9i39E5/JmLfDPuVXLGPDyiXs8+jE7WWzD76WT33x+584X219I9nRjE+M3qr95Shi9eXUnnkHsUPPbTOW+iUvs3nzRvY4Kty+7HUYOBoKSgB4c9FHjB81mFis7X4bgKZmT/mAgNVbanh50Xq+duTIbh3Ftqmyjqq6Jj3yPkFp29xkZhnAEuBkoAx4G/iquy9oa38liT7KHeq2wUcz8H/9juYNS6Cpnoy6xGojXWVxcwnLMscwqfmfcfeZFTmICc3vf6K83POIWQPzi04hPyeLfVc+3ObxGyNF5HoVuR5MP1tPlOaMLNY2F7K6cAL9DphIVlYW61cs4rgPf8OTzZ+m+LOX0GCZDMvcxuDXprJ4n0ux8hWMW/c4S4s/R/3woxlUWABLniW2eRFzB5zM0L0OoKByGdsyBzKoZB9WvP86qyrqGVw8mJLaJXh2f5rHnExBvxiL3n2D6kYYt89eVDZmsGr+v6jM3ZMD6ucyYOULbPZ81maNoGj/z9JQchRD8mNUvPckQw/8LFvqmok2VvKRD6G8vJzY7FuJDR5DfhQGr34ZO+pSavP2pHjkvlRWbGbTG3cx8PBzqK2p5u2KAsYURth7cD4Z+cU0WSYVZYuIZTj9Hw0eSPmP2Nkc87mzqF70IsVHnktNbS2D9hhFQ10VTz73LEcOy6KxYE8GDComs7mOt//1IvsOHcAeb1/PnMN/zcFHfo6auhpqN65k7sL51DYaxx5zHA0ZOfTLaKK6fA1b505nTeFh7Bspo9ZixIpHs/L5W5iTdxxfnPg5Vj9zAwuHf5mxY/elIKOB0ndf4fATv0RzZoy87ChYhMbNy/j3U/dQs66U55sm8MtLv0xFXTOD9yiBaC7lc6fzzpx3KB53EsV5UYbsuR+R7DzK3nuZ8sVvUB/N54Cz/ouc3M71z6VzkjgauNbdJ4brUwHc/X/b2l9JQhLSWA9bVgSPTK/bBs2N+Ju30tTcTHNmDlkfvtDpU5d5ESW2sQuDFUnMnOIzGX/ZvZ06Np37JIYDK1utlwFHtt7BzCYDkwFGjow/XafIdplZUDQ2eIVsv1O75Ie/JN6GpkaIZEBTA0Qy8Mr11NY30Fy7DRoqicQK2Fq+kcasfDIaq8lqrCIvrx9b162kLppPUUF/Nlc3UrFlE3UNTQzf+yC2rFxAdlMl1q+Iiqoa6jeX0RjNo1//QUQyo2yrrmXr1gqiWTlsqGlkeH6U6opNDIw5GRmZ+PI3WDfsc8Soo85iZKybi+cPozCrmcWNQxnMJjbURhgyZCiFm97lo9h+NBGhsrqKjVVN7Dkwh6yIE82IsLVsAcOGDsOz89m6oYwFVf05anAj/XOzGZqXwcx1EWqrthDJHUgzEVYtW8iYUaNZU76NwtwosX6FrKuoZu/8Bj6qaCA7w2iORMmrXM6K5sGMGpRLZU0NVSvfJ1I8FvNmCoqGUV4fYY+MbTTVVLC+IZsRBVFqmqNQMJyB5e+xZt16MvOKqKmppbFwJL55Bf2H7kVTUxORZf+kmhwyCoezNXsIY3KqqSqbx4f9DmOPgflk16yjwSNsqHZKWM9HPph6y2KPyoVEC4aQF4uSUVdBXtRYXt+f7MwIWZFm9mxaQUb1Rub1/wwZ3sjWDR+x3EYSiRhjsiuIVK0nb9BQtlk+/WLZWFMdsbpN1G4uI3/MUdSuXsD6pjyW1+ZydEk2DR5hQON6lm5qIK+pgghOU9E+FORmE6mvZMXajeRTzagDz++Cn+CO9bSaxDnAJHf/Vrj+deBId/9uW/urJiEisuvS+QF/q4ARrdZLwjIREUmBnpYk3gbGmtloM8sCzgOeSHFMIiJ9Vo/qk3D3RjP7LvAcwRDYu9x9forDEhHps3pUkgBw9+nA9FTHISIiPa+5SUREehAlCRERiUtJQkRE4lKSEBGRuHrUzXS7ysw2AJ98bnRiioDe9DwFXU/Ppuvp2fra9ezp7gnN4pXWSWJ3mNmsRO84TAe6np5N19Oz6XriU3OTiIjEpSQhIiJx9eUkcXuqA+hiup6eTdfTs+l64uizfRIiItKxvlyTEBGRDihJiIhIXH0ySZjZJDNbbGalZjYl1fEkysyWm9n7ZjbHzGaFZQPN7AUzWxq+DwjLzcymhdc418wOS230YGZ3mdl6M5vXqmyX4zezC8P9l5rZham4ljCOtq7nWjNbFX5Hc8zs1FbbpobXs9jMJrYqT/nPo5mNMLNXzGyBmc03syvC8rT8ftq5nnT9fmJm9paZvRdez3Vh+WgzmxnG9lA4xQJmlh2ul4bbR7U6V5vXGZe796kXwSPIPwD2ArKA94ADUh1XgrEvB4p2KrsBmBIuTwF+FS6fCjwDGHAUMLMHxP9Z4DBgXmfjBwYCH4bvA8LlAT3oeq4FftTGvgeEP2vZwOjwZzCjp/w8AkOBw8LlfGBJGHNafj/tXE+6fj8G5IXLUWBm+O/+MHBeWH4b8J/h8neA28Ll84CH2rvO9j67L9YkjgBK3f1Dd68H/gqcmeKYdseZwD3h8j3AWa3K7/XAm0ChmQ1NRYAt3P01YPNOxbsa/0TgBXff7O7lwAvApORH/0lxrieeM4G/unuduy8DSgl+FnvEz6O7r3H3d8LlbcBCgjnn0/L7aed64unp34+7e2W4Gg1fDpwIPBKW7/z9tHxvjwAnmZkR/zrj6otJYjiwstV6Ge3/8PQkDjxvZrPNbHJYNsTd14TLa4Eh4XK6XOeuxp8O1/XdsAnmrpbmGdLoesKmiUMJ/lpN++9np+uBNP1+zCzDzOYA6wmS7wfAFndvbCO27XGH2yuAQXTievpikkhnx7r7YcApwGVm9tnWGz2oT6btmOZ0jz90KzAGGA+sAX6b2nB2jZnlAX8HvufuW1tvS8fvp43rSdvvx92b3H08UELw1/9+3fG5fTFJrAJGtFovCct6PHdfFb6vBx4j+EFZ19KMFL6vD3dPl+vc1fh79HW5+7rwP3MzcAcfV+V7/PWYWZTgF+oD7v5oWJy2309b15PO308Ld98CvAIcTdDM1zLDaOvYtscdbi8ANtGJ6+mLSeJtYGw4KiCLoFPniRTH1CEz62dm+S3LwOeBeQSxt4wguRB4PFx+AvhGOArlKKCiVbNBT7Kr8T8HfN7MBoRNBZ8Py3qEnfp9zib4jiC4nvPCUSejgbHAW/SQn8ewvfpOYKG739hqU1p+P/GuJ42/n2IzKwyXc4CTCfpZXgHOCXfb+ftp+d7OAV4Oa4LxrjO+7u6l7wkvgpEZSwja9H6c6ngSjHkvglEJ7wHzW+ImaGd8CVgKvAgM9I9HQ/whvMb3gQk94Br+QlDFbyBoC724M/ED/0HQ4VYKXNTDrue+MN654X/Ioa32/3F4PYuBU3rSzyNwLEFT0lxgTvg6NV2/n3auJ12/n4OBd8O45wHXhOV7EfySLwX+BmSH5bFwvTTcvldH1xnvpcdyiIhIXH2xuUlERBKkJCEiInEpSYiISFxKEiIiEpeShIiIxKUkIdIOM2tq9cTQOV35FFAzG2WtniAr0hNldryLSJ9W48GjEET6JNUkRDrBgrk9brBgfo+3zGzvsHyUmb0cPkDuJTMbGZYPMbPHwvkA3jOzT4enyjCzO8I5Ap4P76YV6TGUJETal7NTc9NXWm2rcPeDgN8DN4dltwD3uPvBwAPAtLB8GvBPdz+EYA6K+WH5WOAP7n4gsAX4UpKvR2SX6I5rkXaYWaW757VRvhw40d0/DB8kt9bdB5nZRoJHPTSE5WvcvcjMNgAl7l7X6hyjCOZeGBuuXwVE3f3nyb8ykcSoJiHSeR5neVfUtVpuQv2E0sMoSYh03ldavc8Il/9N8KRQgK8Br4fLLwH/CdsnjynoriBFdof+ahFpX044G1iLZ929ZRjsADObS1Ab+GpYdjnwZzO7EtgAXBSWXwHcbmYXE9QY/pPgCbIiPZr6JEQ6IeyTmODuG1Mdi0gyqblJRETiUk1CRETiUk1CRETiUpIQEZG4lCRERCQuJQkREYlLSUJEROL6/52/YIAmmiYBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad781ca110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training history\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "plt.title('Training History'),\n",
    "plt.xlabel('Epoch'),\n",
    "plt.ylabel('Validation Loss')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Training Set R-Square=', 0.9949287381599607)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad387f8090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot actual vs prediction for training set\n",
    "TestResults = numpy.genfromtxt(\"trainresults.csv\", delimiter=\",\")\n",
    "plt.plot(Y1,TestResults,'ro')\n",
    "plt.title('Training Set')\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "\n",
    "# Compute R-Square value for training set\n",
    "TestR2Value = r2_score(Y1,TestResults)\n",
    "print(\"Training Set R-Square=\", TestR2Value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Validation Set R-Square=', 0.9916464732839869)\n"
     ]
    },
    {
     "data": {
      "image/png": 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2Kb29o7cIXJxlNqpxE0FE/BuApM+PaF58U5L7aqwzLVmy8xgBuDjLbBz1VhZPk/S6ykp6uIwfV2mdyY+ENGtIvZXFfw6slLQaEDAH+NPcojKbLD8S0qxu9RaU3SnpMOCItOmhiHgxv7DMzKxV6n1UZQ/wF8CHI+J+oFfSqblGZmZmLVHvGMFXgZfInicA2TME/ncuEZmZWUvVmwheHxF/A/waICK2ko0VmJlZl6s3EbyUHi4TAJJeD3iMwMysAOq9a+hS4E7gYEmDwLHAeXkFZWZmrVMzEUgS8BBZdfE7yLqELoyIjTnHZmZmLVAzEURESPp2RLwJuKMFMZmZWQvVO0bwY0lvyzUSMzNri3rHCN4OnCtpGHiBrHsoIuLNeQVmZmatUW8i+J1cozAzs7ap9TyCPYEPAocCDwBXR8S2VgRmZmatUWuMYBnQR5YETgY+n3tEZmbWUrW6hualu4WQdDVwT/4hmZlZK9VqEfy68sJdQtY2g4PZc4inTMmWg4PtjsisUGq1CI6U9Hx6LWCvtF65a+g1Y31Q0sHAtcBssqkpBiJiqaQZwI3AXGAYOCsinp3UUVhxDQ7u/LSxNWuydfDzBsyaZNwWQURMjYjXpH/TI2K3qtdjJoFkG/CxiJhHVpH8IUnzgEuAFRFxGLAirZtlRl79X3jhzo+chGx98eJ2RGdWSPXePtqwiFgHrEuvN0t6EDgQOB04Lr1tGbASuDivOKyLjHb1P5bHHmtNTGYlUG9l8aRImgu8BbgbmJ2SBMBTZF1Ho31moaQhSUMbNmxoRZjWbosX73r1P5be3nxjMSuR3BOBpFcDtwAXRcTz1fsiIkhTW48UEQMR0RcRfbNmzco7TOsE9V7l9/TAkiX5xmJWIrkmAkm7kyWBwYj4Rtr8tKT90/79gfV5xmBdZKyr/JkzYc4ckLLlwIAHis2aKLdEkKavvhp4MCK+ULXrdmBBer0AuC2vGKzLLFmSXe1X6+mBpUtheBi2b8+WTgJmTZVni+BY4H3A8ZLuS//eC1wGnCjpEeCEtG6WneAHBnz1b9ZiyrrpO1tfX18MDQ21Owwzs64iaVVE9NV6X0vuGjIblyuHzdoqtzoCs7q4ctis7dwisPYarXbAlcNmLeVEYO01Vu2AK4fNWsaJwNprrNoBVw6btYwTgbXXWLUDrhw2axknAmsv1w6YtZ3vGrL26+/3id+sjdwiMDMrOScCM7OScyKwzuIqY7OW8xiBdQ5XGZu1hVsE1jlcZWzWFk4E1jlcZWzWFk4E1jlcZWzWFk4E1jlcZWzWFk4E1jlcZWzWFr5ryDqLq4zNWs4tAjOzknMiMDMrOScCczWvWck5ERRZ5QQvwW67ZcuRJ/pKNe+aNRCxo5rXycCsNJwIiqr6BA/w8svZcuSJ3tW8ZqXnRFBUo53gK6pP9K7mNSs9J4KiqnUir7QUXM1rVnq5JQJJ10haL+mnVdtmSFou6ZG03Dev3y+9GTPG3z91arZ0Na9Z6eXZIvgacNKIbZcAKyLiMGBFWrd2qIwZuJrXrPQUEfl9uTQX+FZEvDGtPwwcFxHrJO0PrIyIw2t9T19fXwwNDeUWZyFNmZLdBTSWOXNgeLhl4ZhZ60laFRF9td7X6jGC2RGxLr1+Cpg91hslLZQ0JGlow4YNrYmuSMbr43fXj5lVadtgcWRNkTEvWSNiICL6IqJv1qxZLYysIEbr+weYOdNdP2a2k1YngqdTlxBpub7Fv98ZRlbyLlq0Y32//bJ/k63yHa3v//rrYeNGJwEz20mrxwj+D7ApIi6TdAkwIyI+Uet7CjVGMPK5vLX09PgK3swmpO1jBJK+DvwHcLiktZLOBy4DTpT0CHBCWi+X8Qq9RrN1K1x4YX7xmFnp5fY8gog4Z4xd8/P6za4wkYrdTZuyloRbBWaWA1cWt1qtQq+xeO4fM8uJE0Gz5TWls+f+MbOc+FGVzTRyILgy0yfs6NZ55pmxPy9ly9EG8D33j5nlxC2CZqpnSuexTuhz5sD27XDddZ77x8xayomgmeqZ0rnWJG+e+8fMWsyJoJnGutqP2DFeUM+Jvr8/mwdo+/Zs6SRgZjnyGEEzDA5m9/pv2jT2e0aOF/jkbmYdwi2Ciai+M2i//eD97x8/CVS4OMzMOpATQaNGPux906asC6deleIwM7MO4UTQqEaniBjrO8zMOoQTQaOaUdjl4jAz6yBOBI2qt7Brjz1g2rTJfYeZWQs4EYxlrKkixnrgS7WZM+Gaa+DKK10cZmYdz7ePjma0qSLOPTe7O2j79uxKX9p1Koixnh2weHHWHdTbmyUB3zpqZh3EiQCyE3/1yXrLltEHhCt3B73wwq77Zs6EpUt3Pcm7ZsDMOpwTwWhX/xPlE76ZdSGPETTjdlBwfYCZda1yJoLqgeDJtABGcn2AmXWh8nUNNfrw+Ea4PsDMulC5EsHgICxYAC+/nM/3uz7AzLpQebqGKi2BZiSBmTOzgrFqrg8wsy5VnkTQrEFhCTZuzArG/PAYMyuAcnQNDQ42b1C40v3j+gAzK4jitwgWLcqqgpvB3T9mVkDFTgSDg/CVr0z889OmZeMB7v4xswJrSyKQdJKkhyU9KumS3H7ogx+c2OcuuCCbR2jLlmw8wM8ONrMCa3kikDQVuAI4GZgHnCNpXtN/6IQTshN5I6ZNg+uvh7/7u6aHY2bWqdoxWHwM8GhErAaQdANwOvDzpv3CokWwYkX9758/H/7lX5r282Zm3aQdXUMHAo9Xra9N23YiaaGkIUlDGzZsaOwXrryyvvftuWfWAnASMLMS69jbRyNiABgA6Ovrixpv31k9D5OfNw9+9rOJhGZmVijtaBE8ARxctX5Q2tY68+c7CZiZJe1IBD8CDpN0iKQ9gLOB21v265K7gszMqrQ8EUTENuDDwHeBB4GbIqK5l+czZ46977rrmvpTZmbdri11BBHx7Yh4Q0S8PiKaX6q7dOmuk8JBVh/gWgAzs50Us7K4v3/XSeFcH2BmNqqOvWto0jwpnJlZXYrZIjAzs7o5EZiZlZwTgZlZyTkRmJmVnBOBmVnJKaKxaXzaQdIGYKLPmtwP2NjEcDpFUY8LintsRT0uKO6xdftxzYmIWbXe1BWJYDIkDUVEX7vjaLaiHhcU99iKelxQ3GMr6nGN5K4hM7OScyIwMyu5MiSCgXYHkJOiHhcU99iKelxQ3GMr6nHtpPBjBGZmNr4ytAjMzGwcTgRmZiVX2EQg6SRJD0t6VNIl7Y5nMiRdI2m9pJ9WbZshabmkR9Jy33bGOBGSDpZ0l6SfS/qZpAvT9iIc256S7pF0fzq2v07bD5F0d/q7vDE9pa/rSJoq6V5J30rrRTmuYUkPSLpP0lDa1vV/j7UUMhFImgpcAZwMzAPOkTSvvVFNyteAk0ZsuwRYERGHASvSerfZBnwsIuYB7wA+lP53KsKxvQgcHxFHAkcBJ0l6B3A58MWIOBR4Fji/jTFOxoVkTxisKMpxAfx2RBxVVT9QhL/HcRUyEQDHAI9GxOqIeAm4ATi9zTFNWER8H3hmxObTgWXp9TLgjJYG1QQRsS4ifpxebyY7sRxIMY4tImJLWt09/QvgeODmtL0rj03SQcApwFVpXRTguMbR9X+PtRQ1ERwIPF61vjZtK5LZEbEuvX4KmN3OYCZL0lzgLcDdFOTYUvfJfcB6YDnwC+C59Nxu6N6/yy8BnwC2p/WZFOO4IEvW35O0StLCtK0Qf4/jKe4TykokIkJS194HLOnVwC3ARRHxfHaBmenmY4uIl4GjJO0D3Aoc0eaQJk3SqcD6iFgl6bh2x5ODd0XEE5JeCyyX9FD1zm7+exxPUVsETwAHV60flLYVydOS9gdIy/VtjmdCJO1OlgQGI+IbaXMhjq0iIp4D7gLeCewjqXIB1o1/l8cCp0kaJutyPR5YSvcfFwAR8URaridL3sdQsL/H0RQ1EfwIOCzdybAHcDZwe5tjarbbgQXp9QLgtjbGMiGpb/lq4MGI+ELVriIc26zUEkDSXsCJZGMgdwFnprd13bFFxCcj4qCImEv2/6t/jYh+uvy4ACRNkzS98hp4D/BTCvD3WEthK4slvZesL3MqcE1ELGlzSBMm6evAcWRT4j4NXAr8M3AT0Es2RfdZETFyQLmjSXoX8O/AA+zob/4U2ThBtx/bm8kGFqeSXXDdFBGflvQ6sivpGcC9wLkR8WL7Ip241DX08Yg4tQjHlY7h1rS6G/CPEbFE0ky6/O+xlsImAjMzq09Ru4bMzKxOTgRmZiXnRGBmVnJOBGZmJedEYGZWck4EZoCkMySFpHGrfyWdJ+mASfzOcZUZO806hROBWeYc4AdpOZ7zgAknArNO5ERgpZfmOnoX2dTJZ1dtvzjNTX+/pMsknQn0AYNpvvq90vz1+6X390lamV4fI+k/0pz9P5R0eOuPzKw+nnTOLJtm+M6I+H+SNkl6K/DatP3tEbFV0oyIeEbSh8mqaSsPLRnrOx8C3h0R2ySdAHwG+IP8D8WscU4EZll30NL0+oa0LuCrEbEVYAJTCuwNLJN0GNnUxrs3KVazpnMisFKTNINsBs03pemFp5KduP+pzq/Yxo4u1j2rtv8v4K6I+L30rIWVzYjXLA8eI7CyOxO4LiLmRMTciDgY+CXwK+ADknrglYQBsBmYXvX5YeCt6XV118/e7JiK+bx8QjdrDicCK7tz2DHjZMUtwP5k0w8PpaeMfTzt+xrw95XBYuCvgaXpQecvV33H3wCflXQvbnlbh/Pso2ZmJecWgZlZyTkRmJmVnBOBmVnJORGYmZWcE4GZWck5EZiZlZwTgZlZyf0X0hyy2L/Dpt4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad35ef78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot actual vs prediction for validation set\n",
    "ValResults = numpy.genfromtxt(\"valresults.csv\", delimiter=\",\")\n",
    "plt.plot(Y2,ValResults,'ro')\n",
    "plt.title('Validation Set')\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "\n",
    "# Compute R-Square value for validation set\n",
    "ValR2Value = r2_score(Y2,ValResults)\n",
    "print(\"Validation Set R-Square=\",ValR2Value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
